Exploratory Data Analysis(EDA) for Alzheimer disease dataset
Chaeeun Shin
Introduction
This exploratory data analysis (EDA) examines a synthesized Alzheimer’s disease dataset to understand patient demographics and clinical characteristics prior to formal statistical analysis and modeling. The workflow includes systematic data cleaning, recoding of misclassified categorical variables, removal of non-informative identifiers, and verification of missingness. Distributions of continuous variables are visualized using histograms and density plots, both overall and stratified by Alzheimer’s diagnosis status, to assess group-level differences and potential distributional assumptions. Categorical variables are summarized through frequency tables and bar plots, with additional stratification by diagnosis to highlight patterns in comorbidities and reported symptoms. This analysis provides a structured and reproducible foundation for subsequent inferential and predictive modeling.
Package Install
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.2
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(ggplot2)Reading data
setwd("/Users/chaeeunshin/Desktop/AlzAnalysis/data")
data <- read.csv("alzheimers_disease_data.csv")
head(data)## PatientID Age Gender Ethnicity EducationLevel BMI Smoking
## 1 4751 73 0 0 2 22.92775 0
## 2 4752 89 0 0 0 26.82768 0
## 3 4753 73 0 3 1 17.79588 0
## 4 4754 74 1 0 1 33.80082 1
## 5 4755 89 0 0 0 20.71697 0
## 6 4756 86 1 1 1 30.62689 0
## AlcoholConsumption PhysicalActivity DietQuality SleepQuality
## 1 13.297218 6.3271125 1.3472143 9.025679
## 2 4.542524 7.6198845 0.5187671 7.151293
## 3 19.555085 7.8449878 1.8263347 9.673574
## 4 12.209266 8.4280014 7.4356041 8.392554
## 5 18.454356 6.3104607 0.7954975 5.597238
## 6 4.140144 0.2110616 1.5849220 7.261953
## FamilyHistoryAlzheimers CardiovascularDisease Diabetes Depression HeadInjury
## 1 0 0 1 1 0
## 2 0 0 0 0 0
## 3 1 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 1 0 0
## Hypertension SystolicBP DiastolicBP CholesterolTotal CholesterolLDL
## 1 0 142 72 242.3668 56.15090
## 2 0 115 64 231.1626 193.40800
## 3 0 99 116 284.1819 153.32276
## 4 0 118 115 159.5822 65.36664
## 5 0 94 117 237.6022 92.86970
## 6 0 168 62 280.7125 198.33463
## CholesterolHDL CholesterolTriglycerides MMSE FunctionalAssessment
## 1 33.68256 162.18914 21.463532 6.518877
## 2 79.02848 294.63091 20.613267 7.118696
## 3 69.77229 83.63832 7.356249 5.895077
## 4 68.45749 277.57736 13.991127 8.965106
## 5 56.87430 291.19878 13.517609 6.045039
## 6 79.08050 263.94365 27.517529 5.510144
## MemoryComplaints BehavioralProblems ADL Confusion Disorientation
## 1 0 0 1.72588346 0 0
## 2 0 0 2.59242413 0 0
## 3 0 0 7.11954774 0 1
## 4 0 1 6.48122586 0 0
## 5 0 0 0.01469122 0 0
## 6 0 0 9.01568628 1 0
## PersonalityChanges DifficultyCompletingTasks Forgetfulness Diagnosis
## 1 0 1 0 0
## 2 0 0 1 0
## 3 0 1 0 0
## 4 0 0 0 0
## 5 1 1 0 0
## 6 0 0 0 0
## DoctorInCharge
## 1 XXXConfid
## 2 XXXConfid
## 3 XXXConfid
## 4 XXXConfid
## 5 XXXConfid
## 6 XXXConfid3. Conversion to categorical data
data$Gender <- as.factor(data$Gender)
data$Ethnicity <- as.factor(data$Ethnicity)
data$EducationLevel <- as.factor(data$EducationLevel)
data$Smoking <- as.factor(data$Smoking)
data$FamilyHistoryAlzheimers <- as.factor(data$FamilyHistoryAlzheimers)
data$CardiovascularDisease <- as.factor(data$CardiovascularDisease)
data$Diabetes <- as.factor(data$Diabetes)
data$Depression <- as.factor(data$Depression)
data$HeadInjury <- as.factor(data$HeadInjury)
data$Hypertension <- as.factor(data$Hypertension)
data$MemoryComplaints <- as.factor(data$MemoryComplaints)
data$BehavioralProblems <- as.factor(data$BehavioralProblems)
data$Confusion <- as.factor(data$Confusion)
data$Disorientation <- as.factor(data$Disorientation)
data$PersonalityChanges <- as.factor(data$PersonalityChanges)
data$DifficultyCompletingTasks <- as.factor(data$DifficultyCompletingTasks)
data$Forgetfulness <- as.factor(data$Forgetfulness)
data$Diagnosis <- as.factor(data$Diagnosis)
data <- data %>% dplyr::select(-c(PatientID, DoctorInCharge))Identifying missing data
missing_summary <- sapply(data, function(x) sum(is.na(x)))
cat("\nMissing Data Summary\n")##
## Missing Data Summaryprint(missing_summary)## Age Gender Ethnicity
## 0 0 0
## EducationLevel BMI Smoking
## 0 0 0
## AlcoholConsumption PhysicalActivity DietQuality
## 0 0 0
## SleepQuality FamilyHistoryAlzheimers CardiovascularDisease
## 0 0 0
## Diabetes Depression HeadInjury
## 0 0 0
## Hypertension SystolicBP DiastolicBP
## 0 0 0
## CholesterolTotal CholesterolLDL CholesterolHDL
## 0 0 0
## CholesterolTriglycerides MMSE FunctionalAssessment
## 0 0 0
## MemoryComplaints BehavioralProblems ADL
## 0 0 0
## Confusion Disorientation PersonalityChanges
## 0 0 0
## DifficultyCompletingTasks Forgetfulness Diagnosis
## 0 0 0saveRDS(data,'data.rds')Distribution of numeric variables
cat("\nHistograms of Continuous Variables:\n")##
## Histograms of Continuous Variables:continuous_columns <- data[, sapply(data, is.numeric)]
width <- c(Age = 10,BMI = 5,AlcoholConsumption = 5,PhysicalActivity = 2,DietQuality = 2,SleepQuality = 2,SystolicBP = 10,DiastolicBP = 10,CholesterolTotal = 30,CholesterolLDL = 30,CholesterolHDL = 10,CholesterolTriglycerides = 70,MMSE = 5, FunctionalAssessment = 2,ADL = 2)
for (col in colnames(continuous_columns)) {
binwidth <- width[[col]]
p <- ggplot(data, aes_string(x=col)) +
geom_histogram(binwidth = binwidth, fill = 'skyblue', color = 'white') +
labs(title = paste('Distribution of', col),
x = col,
y = "Frequency") +
theme_minimal()
print(p)
}## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Histograms of numeric data are generated.
Distribution of numeric variables, based on diagnosis status
data_vis <- data %>%
mutate(
Diagnosis = factor(
Diagnosis,
levels = c(0, 1),
labels = c("Does Not Have Alzheimer's", "Has Alzheimer's")
)
)
cat("\nDensity Plots of Continuous Variables by Diagnosis:\n")##
## Density Plots of Continuous Variables by Diagnosis:for (col in colnames(continuous_columns)) {
p <- ggplot(data_vis, aes(x = .data[[col]], fill = Diagnosis)) +
geom_density(alpha = 0.5) +
labs(title = paste("Density of", col, "by Diagnosis"), x = col) +
scale_fill_manual(values = c("skyblue", "deepskyblue4")) +
theme_minimal()
print(p)
}
Distribution of categorical variables
cat("\nHistograms of Categorical Variables:\n")##
## Histograms of Categorical Variables:categorical_variables <- data[sapply(data, is.factor)]
categorical_variables$Gender <- ifelse(categorical_variables$Gender==0,'Male','Female')
categorical_variables <- categorical_variables %>%
mutate(Ethnicity = case_when(
Ethnicity == 0 ~ 'Caucasian',
Ethnicity == 1 ~ 'African American',
Ethnicity == 2 ~ 'Asian',
TRUE ~ 'Other'),
EducationLevel = case_when(
EducationLevel == 0 ~ 'None',
EducationLevel == 1 ~ 'High School',
EducationLevel == 2 ~ "Bachelor's",
TRUE ~ 'Higher')
)
categorical_variables[,4:18] <- ifelse(categorical_variables[4:18]==0,'No','Yes')
for (c in 1:ncol(categorical_variables)) {
counts <- categorical_variables %>%
count(!!sym(colnames(categorical_variables)[c])) %>%
mutate(Percentage = n / sum(n) * 100)
p <- ggplot(counts, aes_string(x=colnames(categorical_variables)[c], y = "n")) +
geom_bar(stat = "identity", fill = "skyblue", color = "black") +
geom_text(aes(label = paste0(round(Percentage,1),"%")), vjust = -0.5) +
labs(title = paste("Bar Plot of", colnames(categorical_variables)[c]),
x = colnames(categorical_variables)[c],
y = "Count") + theme_minimal()
print(p)
}
Distribution of categorical variables, based on diagnosis status
cat("\nHistograms of Categorical Variables based on Diagnosis:\n")##
## Histograms of Categorical Variables based on Diagnosis:for (c in 1:ncol(categorical_variables)) {
if (colnames(categorical_variables)[c]=='Diagnosis') next
counts <- categorical_variables %>%
count(!!sym(colnames(categorical_variables)[c]), Diagnosis)
p <- ggplot(counts, aes_string(x = colnames(categorical_variables)[c], y = "n", fill = "Diagnosis")) +
geom_bar(stat = "identity", position = "stack", color = "black") +
labs(title = paste("Stacked Bar Plot of", colnames(categorical_variables)[c], "by Diagnosis"),
x = colnames(categorical_variables)[c],
y = "Count",
fill = "Diagnosis") +
theme_minimal() +
scale_fill_manual(values = c("Yes" = "skyblue", "No" = "deepskyblue4"))
print(p)
}
